Signal Characteristics Research Articles

Design and Study of Pulsed Eddy Current Sensor for Detecting Surface Defects in Small-Diameter Bars

The design and study of pulsed eddy current sensors for detecting surface defects in small-diameter rods are highly significant. Accurate detection and identification of surface defects in small-diameter rods may be attained by the ongoing optimization of sensor design and enhancement of detection technologies. This article presents the construction of a non-coaxial differential eddy current sensor (Tx-Rx sensor) and examines the detection of surface defects in a small diameter bar. A COMSOL 3D model is developed to examine the variations in eddy current distribution and defect signal characteristics between the plate and rod components. The position of the excitation coil on the bar and the eddy current disruption around the defect are examined. Additionally, a Tx-Rx sensor has been developed and enhanced concerning coil dimensions, coil separation, and elevation height. An experimental system is established to detect bar structures with surface defects of varying depths, and a model correlating differential signal attenuation with defect depth is proposed, achieving a quantitative relative error of less than 5%, thereby offering a reference for the quantitative detection of bar surface defects.

Signal Reconstruction Based on Time‐Varying Sliding Window in WSNs Using Compressed Sensing

ABSTRACTThis paper presents a new algorithm that utilizes compressed sensing (CS) for reconstruction of wireless sensor networks (WSNs) data with spatial and temporal correlation. The proposed method utilizes a time‐varying sliding window mechanism that dynamically adjusts both the window size and the number of measurements. This flexibility allows the algorithm to exploit spatio‐temporal correlations effectively, ensuring that data within the window remains sparse and thus more compressible. By dynamically varying the number of measurements, the algorithm equitably distributes the sampling rate across different time slots, adapting to changes in signal characteristics and minimizing transmission costs. Simulation results demonstrate that our proposed algorithm outperforms other CS reconstruction methods by achieving higher reconstruction precision while requiring fewer transmissions. This is achieved through a decentralized data‐window framework that maximizes the use of prior signal information, leading to improved signal recovery performance in diverse WSN scenarios.

Phase Spectrum Smoothing Demodulation: A New Frontier in eLoran Signal Processing for Enhanced Performance

In the field of modern navigation and positioning, the ground-based eLoran system, serves as a vital backup to the global navigation satellite system (GNSS), which is crucial for numerous key applications. Signal demodulation, integral to eLoran’s precision timing and information transmission, significantly affects system performance. Aiming at the pulse position modulation (PPM) characteristics of eLoran signals, this paper introduces an innovative phase spectrum smoothing demodulation (PSSD) algorithm, crafted to improve demodulation performance under complex noisy and interference-laden conditions. Following a systematic review of existing demodulation techniques in eLoran, this paper details the theoretical foundation, key steps, and significant impact of parameter selection for the PSSD algorithm. Then, the unique advantages in dealing with noise, continuous wave, and skywave interference are analyzed and verified. Through extensive experimental validation under various SNR and interference conditions, the PSSD algorithm shows significant superiority in demodulation performance compared with the traditional envelope phase detection (EPD) algorithm. The effectiveness of the PSSD algorithm in interference mitigation and its stable performance across diverse conditions confirm its potential to meet the high-precision timing requirements of eLoran systems, contributing to the advancement of modern communication systems.

ON THE SIMULTANEOUS DETERMINATION OF THE DENSITY DISTRIBUTION OF EQUIVALENT SOURCES UNDER AN EXTERNAL FIELD AND THE SPECTRUM OF USEFUL SIGNAL

The article investigates the possibility of simultaneously recovering equivalent sources under an external field and the spectral characteristics of a useful signal. Examples of variational formulations for different versions of the method of linear integral representations are presented, and the problem of finding the density distribution of gravitating or magnetic masses on several horizontal planes is formulated. Additionally, the Fourier transform of the anomalous field element based on the known signal values at certain observation points, complicated by noise, is discussed.

Biometric Technology Based on Wireless Sensing: Principles, Applications, and Challenges

With the rapid advancement of computer technology, image processing, and artificial intelligence, biometric identification technology has seen widespread application, gradually permeating areas such as national security, public security, and home security. However, traditional biometric technologies exhibit certain limitations. In contrast, biometric identification based on wireless sensing offers distinct advantages, including non-contact operation and strong concealment, making it a promising alternative in various security applications. This paper provides a comprehensive overview of the history and current state of biometric technology, alongside the development background of wireless sensing technology. It details the principles of wireless sensing-based biometric identification, covering essential characteristics of wireless signals, and related technologies such as RFID, Wi-Fi, Bluetooth, and millimeter wave technology, as well as relevant signal processing algorithms. Furthermore, the paper analyzes the application of this technology in areas like human posture and motion recognition and physiological feature recognition, supported by practical case studies. Finally, it addresses the environmental dependencies, data privacy, and security concerns associated with this technology, while offering insights into future development directions.

A Quantification Method for Micro-defect Size in Copper Foil Based on Motion-induced Eddy Current Signal Characteristics

Abstract Lithium batteries represent a pivotal technology in modern energy storage and have attracted significant attention. Copper foil is frequently employed as the current collector for the negative electrode; however, the presence of micro-defects can severely impair the electrochemical performance of lithium batteries. Therefore, developing a method that can accurately detect micro-defects during high-speed production processes and subsequently achieve precise quantification of defect sizes is of paramount importance. Motion-Induced Eddy Current (MIEC) techniques, characterized by relative motion, can detect defects in conductive materials during high-speed movement. However, no research has been conducted on defect size quantification based on motion-induced eddy current signals. Consequently, this study proposes a quantification method for micro-defect size in copper foil based on motion-induced eddy current signal characteristics. By combining wavelet soft-thresholding and morphological filtering, high-frequency noise and signal fluctuations caused by variations in lift-off distance are effectively removed from the measured signals. Additionally, eight different features are defined for the micro-defect motion-induced eddy current signals, and a feature dataset for various sizes of copper foil micro-defects is established under different motor speeds. The proposed IVY-CNN-BiLSTM-Attention model is then utilized for the quantification of micro-defect sizes, yielding excellent results.

A Novel Frequency-Division Deep Learning Approach for Magnetotelluric Data Quality Enhancement

High signal-to-noise ratio magnetotelluric (MT) data are crucial for accurately interpreting subsurface structures. Recently, deep learning has become popular for MT denoising due to its ability to avoid parameter tuning and enable real-time processing. These methods typically fit or predict signals in noisy segments after identifying and segmenting signal and noise in the time domain. However, these methods struggle to preserve both low- and high-frequency signals effectively due to high noise levels in these segments. To address this issue, we propose a novel deep learning denoising method that separately recovers low- and high-frequency signals using distinct strategies. Low-frequency signals are fitted using an inverse autoencoder with a channel attention mechanism, effectively removing high-frequency components. High-frequency signals are then predicted using a bidirectional long short-term memory network (BiLSTM) combined with a squeeze-and-excitation (SE) mechanism, enhancing prediction by considering both global and local signal characteristics. Additionally, we introduce the multivariate state estimation technique (MSET) for real-time signal-noise identification. MSET analyzes residuals after separating low-frequency signals to identify noise. Denoising is performed only on segments with significant noise, preserving more effective signals. Finally, the fitted low-frequency dominant component and predicted high-frequency component are combined to form the denoised MT signals. This combined approach significantly improves the restoration quality of effective signals compared to existing methods. Experimental results demonstrate that our method exhibits superior denoising capabilities in both quantitative and qualitative evaluations, including apparent resistivity-phase curves and polarization direction analysis, offering enhanced performance over current deep learning methods.

Application of the Hilbert-Huang transform to evaluate signals in chromatic confocal spectral interferometry

Chromatic confocal spectral interferometry (CCSI) is a hybrid measurement technique that integrates the principles of spectral interferometry and chromatic confocal microscopy. This innovative approach enables scanning-free acquisition of axial dimensions while leveraging interferometric methods to enhance depth accuracy. This feature allows the CCSI signal to be processed using both peak extraction and evaluation of the interferometric optical path length difference. The phase information offers a decreased measurement uncertainty, making it a commonly used approach in existing studies. Methods such as Fourier transform (FT) and wavelet transform (WT) are frequently employed for this purpose. In this paper, we present a signal processing approach based on the Hilbert-Huang transform (HHT). Through simulations and experiments, we compare HHT with Fourier transform (FT) and wavelet transform (WT), demonstrating its stability, noise resistance, and effectiveness in phase extraction for CCSI measurement signals. Additionally, we leverage the characteristics of CCSI signals and the time-frequency analysis capabilities of HHT to address the direction ambiguity problem in white light interferometry.

Experimental Study on Spectral Response Characteristics and Mechanical State of Coal Based on Artificial Acoustic Signals

With the increase in coal mining depth, the stress and strain state of coal and rock mass affects the formation of dangerous zones of dynamic phenomena. In order to study the relationship between the frequency spectrum characteristics of artificial acoustic signals and the stress state of coal and gas pressure, a test device and system that can generate acoustic signals by mechanical vibration excitation are developed by using the design idea of the unit module. Firstly, the basic mechanical parameters of coal under uniaxial compression are analyzed. On this basis, we use the test device to study the qualitative and quantitative relationships between the relative stress coefficient K value of the coal body and the axial loading stress, whether it contains gas, and the mechanical vibration force. The test results show that when the gas-containing coal and the gas-free coal are subjected to the same external mechanical vibration knocking force to stimulate the artificial acoustic signal test, the relative stress coefficient K value increases first and then decreases with the increase in axial loading stress. The relationship between the relative stress coefficient K and the axial loading stress σ can be expressed in the form of exponential function K=e−Cσ. When the axial loading stress and the external mechanical vibration force are both fixed values, the relative stress coefficient K value of the coal body with gas is smaller than that without gas. When the axial loading stress and gas-bearing pressure of the coal body are both fixed values, the relative stress coefficient K value decreases with the increase in the impact force of the external mechanical vibration. This experimental study can provide a reference for the identification and prediction of dynamic disasters based on artificial acoustic signals.

An adaptive learning method for long-term gesture recognition based on surface electromyography.

The surface electromyography (EMG) signal reflects the user's intended actions and has become the important signal source for human-computer interaction. However, classification models trained on EMG signals from the same day cannot be applied for different days due to the time-varying characteristics of the EMG signal and the influence of electrodes shift caused by device wearing for different days, which hinders the application of commercial prosthetics. This type of gesture recogni-tion for different days is usually referred to as long-term gesture recognition.
Approach. To address this issue, we propose a long-term gesture recognition method by optimizing feature extraction, dimensionality reduction, and classification model calibration in EMG signal recognition. Our method extracts differential common spa-tial patterns (CSP) features and then conduct dimensionality reduction with non-negative matrix factorization (NMF), effectively reducing the influence of the non-stationarity of the EMG signals. Based on clustering and classification self-training(CCST) scheme, we select samples with high confidence from unlabeled sam-ples to adaptively updates the model before daily formal use. 
Main results. We verify the feasibility of our method on a dataset consisting of 30 days of gesture data. The proposed gesture recognition scheme achieves accuracy over 90%, similar to the performance of daily calibration with labeled data. However, our method needs only one repetition of unlabeled gestures samples to update the classifi-cation model before daily formal use. 
Significance. From the results we can conclude that the proposed method can not only ensure superior performance, but also greatly facilitate the daily use, which is especially suitable for long-term application.&#xD.

Model-agnostic meta-learning for EEG-based inter-subject emotion recognition.


Developing an efficient and generalizable method for inter-subject emotion recognition from neural signals is an emerging and challenging problem in affective computing. In particular, human subjects usually have heterogeneous neural signal characteristics and variable emotional activities that challenge the existing recognition algorithms from achieving high inter-subject emotion recognition accuracy. 
Approach. 
In this work, we propose a model-agnostic meta-learning algorithm to learn an adaptable and generalizable Electroencephalogram (EEG)-based emotion decoder at the subject's population level. Different from many prior end-to-end emotion recognition algorithms, our learning algorithms include a pre-training step and an adaptation step. Specifically, our meta-decoder first learns on diverse known subjects and then further adapts it to unknown subjects with one-shot adaptation. More importantly, our algorithm is compatible with a variety of mainstream machine learning decoders for emotion recognition.
Main results.
We evaluate the adapted decoders obtained by our proposed algorithm on three Emotion-EEG datasets: SEED, DEAP, and DREAMER. Our comprehensive experimental results show that the adapted meta-emotion decoder achieves state-of-the-art inter-subject emotion recognition accuracy and outperforms the classical supervised learning baseline across different decoder architectures. 
Significance. 
Our results hold promise to incorporate the proposed meta-learning emotion recognition algorithm to effectively improve the inter-subject generalizability in designing future affective brain-computer interfaces (BCIs).

Surface feature extraction method for cloud data of aircraft wall panel measurement points

In the cloud, users need to connect to the data server to perform the file transmission via the Internet, and the Server transmits data to many servers. A machine or vehicle that can fly with the assistance of the air is known as an Aircraft. As an alternative to the downward thrust of jet engines, it uses either static lift or an airfoil's dynamic lift to combat gravity's pull. Drawing wall panel measurement points in the model is easy using the Aircraft Wall Panels (AWP) button. Draw wall panels between existing nodes or on the drawing grid using the relevant wall panel specifications. The technique intends to discover and extract information about undesirable defects such as dents, protrusions, or scratches based on local surface attributes gathered from a 3D scanner. Defects from a perfectly smooth surface include indentations and bumps on the surface. An image's features may be extracted by reducing the number of pixels in the picture to a manageable size so that the most exciting sections of the image can be recorded with Surface Feature Extraction (SFE). Some of the problems are the threat of drones and composite materials that do not break easily in oxymoronic. The aircraft's inner structure may have been damaged, although this is impossible to determine. A runway incursion severely threatens aviation safety because of the rise in aircraft movement on the airport surface and other human factors. An electronic moving map of airport runways and taxiways is shown to the pilot through a head-up display in the cockpit's head-down position. A practical feature extraction approach is required to ensure the safety of the airport scene in runway incursion prevention systems. All the drawbacks are rectified by AWP-SFE sensors installed along the runway centerline to detect magnetic signals generated by surface-moving targets, and this information is utilized to compute the target's length. The target length may extract peak features after regularizing the time domain data. Differentiation of target characteristics is used to determine the similarities between distinct targets. The suggested method's signal characteristics are more easily recognized than time domain or frequency domain feature methods. The experimental results show the proposed method AWP-SE to achieve a high-efficiency ratio of 88.2%, activity ratio of 73.3%, Analysis of aircraft in wall plane measurement point of 87.8% and an error rate of 32.3% compared to other methods.

Comparative Analysis of Color Models for improved rPPG Signals in Remote Blood Pressure Measurement

Abstract The quality of remote photoplethysmography (rPPG) signals presents a significant challenge in contactless optical blood pressure measurement. Feature and morphologybased approaches heavily rely on subtle changes in signal characteristics, but rPPG signals are highly susceptible to noise and interference. This study aims to evaluate rPPG signal quality by assessing correlation with a reference PPG and signal-to-noise ratio (SNR) across various color models and rPPG methods. Analyses are performed under different measurement conditions, accounting for common sources of signal artifacts.

The East Asian monsoon variability in the Nihewan Basin, northern China, during the Early Pleistocene: A grain size end-member modelling analysis

The Pleistocene sediments of the Nihewan Basin in northern China preserve a detailed terrestrial sediment archive for reconstructing the palaeoenvironment and palaeoclimate changes during early times of hominin occupation in E Asia, following the earliest locations outside of Africa. In this study, we investigate the composite 86.2-m long NH-T section of Dachangliang which was formed with an astronomically tuned age between ca. 1.66 and 0.78 Ma. Parameterized grain size end-member modelling analysis is applied for the first time in the Nihewan Basin to unravel the characteristics of sediment sources, transportation dynamics, and climatic signals in the region. The grain-size distributions of the NH-T sediment samples are attributed to a mixture of four distinct end members (EMs 1-4). EM 1 represents a global atmospheric fine silt component (mode at 7.9 μm) which probably resulted from high-altitude westerly transport from distal sources. EM 2, a medium silt component (mode at 27.6 μm) was probably transported by the low-level westerly winds during the spring from a relatively proximal source in comparison to EM 1. EM 3, a coarse silt component (mode at 59.9 μm), represents short distance suspended materials that were blown out by N or NW winds of the East Asian winter monsoon (EAWM). EM 4 is a fine sand component (mode at 221.1 μm), probably representing fluvial deposits carried by overland flow. Therefore, the temporal fluctuations in the abundances of EMs 1-3 are used to infer the history of the East Asian summer monsoon (EASM) (EM 1), of dust-storm outbreaks during springtime (EM 2), and of the EAWM (EM 3). The NH-T climate record shows an overall increase in EM 3 peaks, accompanied by a decrease in EM 1 minima from ca. 1.45 to 0.82 Ma, indicating a long-term aridification and cooling trend in the Nihewan Basin. At ca. 1.25 Ma, the EM patterns transition from short-frequency to longer fluctuations, apparently coinciding with the onset of the Mid-Pleistocene transition (MPT). Periods of stronger EASM with more frequently warm and wet conditions probably occurred in the basin between ca. 1.66-1.62, 1.52-1.25 and after 0.82 Ma. Intensified EAWM conditions probably prevailed in the basin during the intervening periods from ca. 1.62-1.52 and the MPT 1.25-0.82 Ma. The inferred warmer and wetter conditions likely supported hominin activities in the Nihewan Basin, in contrast to mostly colder and drier conditions. Nevertheless, the higher number of discovered Palaeolithic sites and recorded lithic artifacts from the first half of the MPT apparently reflects the successful adaptability of hominins to the prevailing cold climate. The relatively consistent patterns observed between the variations of a median grain-size stack of the Chinese Loess Plateau (CLP MD) and EM 3, and of the magnetic susceptibility stack of the CLP (CLP MS) and EM 1, indicate the climatic sensitivity of these EMs in response to the long-term glacial/interglacial oscillations previously inferred from the CLP. However, significant differences in the CLP MD and CLP MS on the one hand side and in the trends of the EMs 3 and 1 on the other suggest that regional climate conditions varied between the CLP and the Nihewan Basin. Further research is required to explore such regional climate differences and their possibly underlying factors in the Early Pleistocene.

Fast Parameter Estimation of Linear Frequency Modulation Signals in Marine Environments Based on Gradient Optimization Strategy

Multi-buoy sonar systems achieve target localization by receiving broadband Linear Frequency Modulation signals emitted from the transmitter. Accurate estimations of the parameters of Linear Frequency Modulation signals significantly enhance the localization accuracy. Linear Frequency Modulation signals can be focused into the fractional domain through Fractional Fourier Transform, but this increases the computational complexity. In marine environments, the low signal-to-noise ratio and multipath effects degrade the parameter estimation accuracy further. To address these issues, this paper proposes a fast estimation algorithm based on the Fractional Fourier Transform and a Gradient Subtraction-Average-Based Optimizer. This algorithm leverages the impulsive characteristics of Linear Frequency Modulation signals after Fractional Fourier Transform transformation, using the Fractional Fourier Transform as the fitness function. The Gradient Subtraction-Average-Based Optimizer algorithm includes three enhancement strategies: chaotic mapping initialization, a Golden Sine Algorithm, and an adaptive t-distribution variational operator. The simulation results demonstrate that the Gradient Subtraction-Average-Based Optimizer algorithm improves the issues of low diversity in the search agents, imbalanced global and local search capabilities, and susceptibility to local optima. A comparative analysis and statistical testing confirm that under a low signal-to-noise ratio and multipath effect conditions, the Gradient Subtraction-Average-Based Optimizer algorithm not only ensures real-time parameter estimation but also improves the estimation accuracy. The results of the parameter estimation provide reliable data support for subsequent target localization.

A Hardware-Efficient Novelty-Aware Spike Sorting Approach for Brain-Implantable Microsystems.

Unsupervised spike sorting, a vital processing step in real-time brain-implantable microsystems, is faced with the prominent challenge of managing nonstationarity in neural signals. In long-term recordings, spike waveforms gradually change and new source neurons are likely to become activated. Adaptive spike sorters combined with on-implant training units effectively process the nonstationary signals at the cost of high hardware resource utilization. On the other hand, static approaches, while being hardware-friendly, are subjected to decreased processing performance in such recordings where the neural signal characteristics gradually change. To strike a balance between the hardware cost and processing performance, this study proposes a hardware-efficient novelty-aware spike sorting approach that is capable of dealing with both variated spike waveforms and spike waveforms generated from new source neurons. Its improved hardware efficiency compared to adaptive ones and capability of dealing with nonstationary signals make it attractive for implantable applications. The proposed novelty-aware spike sorting especially would be a good fit for brain-computer interfaces where long-term, real-time interaction with the brain is required, and the available on-implant hardware resources are limited. Our unsupervised spike sorting benefits from a novelty detection process to deal with neural signal variations. It tracks the spike features so that in case of detecting an unexpected change (novelty detection) both on and off-implant parameters are updated to preserve the performance in new state. To make the proposed approach agile enough to be suitable for brain implants, the on-implant computations are reduced while the computational burden is realized off-implant. The performance of our proposed approach is evaluated using both synthetic and real datasets. The results demonstrate that, in the mean, it is capable of detecting 94.31% of novel spikes (wave-drifted or emerged spikes) with a classification accuracy (CA) of 96.31%. Moreover, an FPGA prototype of the on-implant circuit is implemented and tested. It is shown that in comparison to the OSORT algorithm, a pivotal spike sorting method, our spike sorting provides a higher CA at significantly lower hardware resources. The proposed circuit is also implemented in a 180-nm standard CMOS process, achieving a power consumption of 1.78[Formula: see text][Formula: see text] per channel and a chip area of 0.07[Formula: see text]mm2 per channel.

Performance Analysis of Semi-refined Digital Forearm Modeling and Simplified Forearm Model in Electromagnetic Simulation

Objective: The objective of this study is to analyze the performance difference between simplified and digital models based on medical images. Methods: According to the characteristics of human anatomy, the finite element simulation software COMSOL Multiphysics 5.5 was employed to construct a simplified arm model using cylinders and a digital arm model based on Chinese digital human regarding electroacupuncture therapy as an example. A comparative analysis was then performed considering three aspects: mesh number, potential distribution, and resource consumption. Results: Through analysis, the digital arm model based on Chinese digital human requires significantly more mesh cells than the simplified arm model in mesh generation. Meanwhile, because the digital arm model based on the Chinese digital human fully expresses the nonuniformity of the tissue distribution in a real human body, its signal distribution in its interior is also relatively scattered, and the coupling potential slightly differs at the electrode vertex with the smallest change. In addition, the digital arm model has much higher resource consumption and computer hardware resource requirements compared with the simplified arm model. Conclusion: As a result, the digital model based on the Chinese digital human can more fully express the tissue distribution and electrical signal characteristics of a real human body. However, due to its high computational requirements, appropriate simplification can be selected to improve the computational efficiency of the model in practical applications.

Data Mining and Analysis for Iodinated Contrast Media Adverse Event Signals Based on the Food and Drug Administration Adverse Event Reporting System Database

BackgroundThe purpose of this study was to employ the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database to mine and analyze adverse events related to iodinated contrast media (ICM), explore the characteristics of adverse events (AEs) including their occurrence and correlation strength between AEs and drugs, and to provide valuable insights for clinical use. MethodsThe FAERS database was queried, data from Q1 of 2004 to Q2 of 2023 were extracted, and AE reports targeting 5 ICMs as the primary suspects were collected. Data mining and analysis were carried out on relevant reports using the reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence propagation neural network (BCPNN), and empirical Bayes geometric mean (EBGM), while the standardized medical dictionary for regulatory activities (MedDRA) queries (SMQ) was used for systematic classification. ResultsA total of 11,155,106 AE reports were retrieved from FAERS, with 2,412 for ioversol, 2,001 for iohexol, 987 for iodixanol, 1,154 for iopamidol, and 3,835 for iopromide. ICM-induced AE occurrence targeted 21 system organ classes (SOCs). A total of 329 significant disproportionality Preferred terms (PTs) conforming to the 4 algorithms were simultaneously retained. The results revealed that the medium and strong adverse drug reaction (ADR) signals of the 5 ICMs largely focused on “respiratory, thoracic and mediastinal disorders,” “general disorders and administration site conditions,” “immune system disorders,” and “skin and subcutaneous tissue disorders.” Ioversol (log2ROR = 1.21, Padj = 0.034) and iopromide (log2ROR = 1.32, Padj = 0.004) were both correlated with a higher incidence of a significant ADR signal, namely throat irritation, particularly in females. In addition, ioversol and iopromide also suggested that toxic nephropathy (log2ROR = −2.47, Padj < 0.001) and hyperhidrosis (log2ROR = −1.22, Padj = 0.001) were significant ADR signals, especially in males, respectively. ConclusionsWhile the AE distribution of the 5 ICMs was consistent, there were variations in specific ADR signal characteristics, warranting further consideration and exploration.

Correlation Analysis Approach Between Features and Motor Movement Stimulus for Stroke Severity Classification of EEG Signal Based on Time Domain, Frequency Domain, and Signal Decomposition Domain

The healing process of a stroke necessitates tools for measuring relevant parameters to facilitate monitoring, evaluation, and medical rehabilitation. Accurate parameter measures can be observed in stroke patients' severity to ascertain suitable interventions by identifying components pertinent to monitoring, evaluation, and medical rehabilitation. The components are derived from the observation collection process utilizing an EEG device, accompanied by a motor stimulus, to ensure the acquisition of EEG signals for monitoring, evaluation, and medical rehabilitation while preventing any loss of information during data collection. The acquired information encounters challenges due to the signal's unstable, nonlinear, and non-stationary characteristics, necessitating efforts to stabilize, render stationary, and linearize it through suitable signal processing and feature extraction techniques to achieve a pertinent feature composition. The subsequent difficulty is achieving the objectives of medical monitoring, evaluation, and rehabilitation, necessitating the correlation between EEG signal characteristics and motor movement stimuli, ensuring that the process adheres to appropriate parameter identification and scheduling per the established plan. In response to this difficulty, a correlation analysis methodology is established, incorporating normalcy tests, significance tests, and correlation analysis to ensure that the relevant factors for identifying stroke severity categorization patterns are precisely identified beforehand. The correlation analysis strategy employs raw data situations, preprocessing, feature extraction, feature selection, and correlation analysis for classification purposes. Our experimental findings indicate that the correlation analysis approach for assessing stroke severity classification patterns is evident in the Hajorth Complexity feature, utilizing the Shoulder motor movement stimulus and the SVM classification type, achieving an accuracy significant value of 98%. These findings confirm the efficacy of correlation analysis between EEG signal features and motor movement stimuli in identifying the optimal parameters within a reduced dimensional space to assess stroke severity effectively.

Combination of rs-fMRI, QSM, and ASL Reveals the Cerebral Neurovascular Coupling Dysfunction Is Associated With Cognitive Decline in Patients With Chronic Kidney Disease.

Neurovascular coupling (NVC) reflects the close connection between neural activity and cerebral blood flow (CBF) responses, providing new insights to explore the neuropathological mechanisms of various diseases. Non-dialysis patients with chronic kidney disease (CKD) exhibit cognitive decline, but the underlying pathological mechanisms are unclear. The prospective study involved 53 patients with stage 1-3a CKD (CKD1-3a), 78 patients with stage 3b-5 CKD (CKD3b-5), and 52 healthy controls (HC). Our investigation involved voxel-based assessments of both global and regional BOLD signal characteristics. Additionally, we explored the correlations between neuroimaging indices, Montreal Cognitive Assessment (MoCA) scores, and clinical laboratory findings. Compared to HC, the CKD3b-5 and CKD1-3a groups exhibited lower ALLF and ReHo in the default mode network (DMN), higher CBF in bilateral hippocampus (HIP), higher susceptibility values in bilateral caudate nucleus (CAU) and putamen (PUT), and lower susceptibility values in bilateral HIP. At the global level, the coupling coefficients were lower in CKD1-3a and CKD3b-5 groups than in HC. At the ROI level, the CBF-ALFF and CBF-ReHo coupling in HIP and basal ganglia regions were lower in CKD3b-5 groups than in the CKD1-3a group. Most importantly, susceptibility-ALFF in ANG.R may mediate the effects of phosphorus on cognitive decompensation in patients with CKD1-3a. Non-dialysis patients with CKD exhibit abnormal NCV, which is associated with the cognitive decline. Specifically, the susceptibility-ALFF may serve as a valuable biomarker for early assessment of cognitive decline in CKD, offering insights into the pathogenesis of cognitive decline in CKD.